wehub-resource-sync
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
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from __future__ import print_function
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from sklearn import datasets
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import matplotlib.pyplot as plt
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import numpy as np
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from mlfromscratch.supervised_learning import ParticleSwarmOptimizedNN
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from mlfromscratch.utils import train_test_split, to_categorical, normalize, Plot
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from mlfromscratch.deep_learning import NeuralNetwork
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from mlfromscratch.deep_learning.layers import Activation, Dense
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from mlfromscratch.deep_learning.loss_functions import CrossEntropy
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from mlfromscratch.deep_learning.optimizers import Adam
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def main():
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X, y = datasets.make_classification(n_samples=1000, n_features=10, n_classes=4, n_clusters_per_class=1, n_informative=2)
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data = datasets.load_iris()
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X = normalize(data.data)
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y = data.target
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y = to_categorical(y.astype("int"))
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# Model builder
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def model_builder(n_inputs, n_outputs):
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model = NeuralNetwork(optimizer=Adam(), loss=CrossEntropy)
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model.add(Dense(16, input_shape=(n_inputs,)))
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model.add(Activation('relu'))
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model.add(Dense(n_outputs))
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model.add(Activation('softmax'))
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return model
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# Print the model summary of a individual in the population
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print ("")
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model_builder(n_inputs=X.shape[1], n_outputs=y.shape[1]).summary()
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population_size = 100
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n_generations = 10
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=1)
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inertia_weight = 0.8
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cognitive_weight = 0.8
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social_weight = 0.8
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print ("Population Size: %d" % population_size)
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print ("Generations: %d" % n_generations)
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print ("")
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print ("Inertia Weight: %.2f" % inertia_weight)
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print ("Cognitive Weight: %.2f" % cognitive_weight)
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print ("Social Weight: %.2f" % social_weight)
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print ("")
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model = ParticleSwarmOptimizedNN(population_size=population_size,
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inertia_weight=inertia_weight,
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cognitive_weight=cognitive_weight,
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social_weight=social_weight,
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max_velocity=5,
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model_builder=model_builder)
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model = model.evolve(X_train, y_train, n_generations=n_generations)
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loss, accuracy = model.test_on_batch(X_test, y_test)
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print ("Accuracy: %.1f%%" % float(100*accuracy))
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# Reduce dimension to 2D using PCA and plot the results
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y_pred = np.argmax(model.predict(X_test), axis=1)
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Plot().plot_in_2d(X_test, y_pred, title="Particle Swarm Optimized Neural Network", accuracy=accuracy, legend_labels=range(y.shape[1]))
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if __name__ == "__main__":
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main() |